Hi,
A partial answer to your questions:
On Mon, Jul 30, 2012 at 10:33 PM, Vlastimil Brom
<vlastimil.brom@gmail.com>wrote:
> Hi all,
> I'd like to ask for some hints or advice regarding the usage of
> numpy.array and especially slicing.
>> I only recently tried numpy and was impressed by the speedup in some
> parts of the code, hence I suspect, that I might miss some other
> oportunities in this area.
>> I currently use the following code for a simple visualisation of the
> search matches within the text, the arrays are generally much larger
> than the sample - the texts size is generally hundreds of kilobytes up
> to a few MB - with an index position for each character.
> First there is a list of spans(obtained form the regex match objects),
> the respective character indices in between these slices should be set
> to 1:
>> >>> import numpy
> >>> characters_matches = numpy.zeros(10)
> >>> matches_spans = numpy.array([[2,4], [5,9]])
> >>> for start, stop in matches_spans:
> ... characters_matches[start:stop] = 1
> ...
> >>> characters_matches
> array([ 0., 0., 1., 1., 0., 1., 1., 1., 1., 0.])
>> Is there maybe a way tu achieve this in a numpy-only way - without the
> python loop?
> (I got the impression, the powerful slicing capabilities could make it
> possible, bud haven't found this kind of solution.)
>>> In the next piece of code all the character positions are evaluated
> with their "neighbourhood" and a kind of running proportions of the
> matched text parts are computed (the checks_distance could be
> generally up to the order of the half the text length, usually less :
>> >>>
> >>> check_distance = 1
> >>> floating_checks_proportions = []
> >>> for i in numpy.arange(len(characters_matches)):
> ... lo = i - check_distance
> ... if lo < 0:
> ... lo = None
> ... hi = i + check_distance + 1
> ... checked_sublist = characters_matches[lo:hi]
> ... proportion = (checked_sublist.sum() / (check_distance * 2 + 1.0))
> ... floating_checks_proportions.append(proportion)
> ...
> >>> floating_checks_proportions
> [0.0, 0.33333333333333331, 0.66666666666666663, 0.66666666666666663,
> 0.66666666666666663, 0.66666666666666663, 1.0, 1.0,
> 0.66666666666666663, 0.33333333333333331]
> >>>
>Define a function for proportions:
from numpy import r_
from numpy.lib.stride_tricks import as_strided as ast
def proportions(matches, distance= 1):
cd, cd2p1, s= distance, 2* distance+ 1, matches.strides[0]
# pad
m= r_[[0.]* cd, matches, [0.]* cd]
# create a suitable view
m= ast(m, shape= (m.shape[0], cd2p1), strides= (s, s))
# average
return m[:-2* cd].sum(1)/ cd2p1
and use it like:
In []: matches
Out[]: array([ 0., 0., 1., 1., 0., 1., 1., 1., 1., 0.])
In []: proportions(matches).round(2)
Out[]: array([ 0. , 0.33, 0.67, 0.67, 0.67, 0.67, 1. , 1. ,
0.67, 0.33])
In []: proportions(matches, 5).round(2)
Out[]: array([ 0.27, 0.36, 0.45, 0.55, 0.55, 0.55, 0.55, 0.55,
0.45, 0.36])
>> I'd like to ask about the possible better approaches, as it doesn't
> look very elegant to me, and I obviously don't know the implications
> or possible drawbacks of numpy arrays in some scenarios.
>> the pattern
> for i in range(len(...)): is usually considered inadequate in python,
> but what should be used in this case as the indices are primarily
> needed?
> is something to be gained or lost using (x)range or np.arange as the
> python loop is (probably?) inevitable anyway?
>Here np.arange(.) will create a new array and potentially wasting memory if
it's not otherwise used. IMO nothing wrong looping with xrange(.) (if you
really need to loop ;).
> Is there some mor elegant way to check for the "underflowing" lower
> bound "lo" to replace with None?
>> Is it significant, which container is used to collect the results of
> the computation in the python loop - i.e. python list or a numpy
> array?
> (Could possibly matplotlib cooperate better with either container?)
>> And of course, are there maybe other things, which should be made
> better/differently?
>> (using Numpy 1.6.2, python 2.7.3, win XP)
>
My 2 cents,
-eat
> Thanks in advance for any hints or suggestions,
> regards,
> Vlastimil Brom
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